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Add support for collating and analysing rejected trades in backtest

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This commit is contained in:
froggleston
2022-12-05 15:34:31 +00:00
parent f28b314266
commit 5a4e99b413
9 changed files with 254 additions and 57 deletions
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118
freqtrade/data/entryexitanalysis.py
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@@ -15,22 +15,30 @@ from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
def _load_signal_candles(backtest_dir: Path):
def _load_backtest_analysis_data(backtest_dir: Path, name: str):
if backtest_dir.is_dir():
scpf = Path(backtest_dir,
Path(get_latest_backtest_filename(backtest_dir)).stem + "_signals.pkl"
Path(get_latest_backtest_filename(backtest_dir)).stem + "_" + name + ".pkl"
)
else:
scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_signals.pkl")
scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_{name}.pkl")
try:
scp = open(scpf, "rb")
signal_candles = joblib.load(scp)
logger.info(f"Loaded signal candles: {str(scpf)}")
rejected_trades = joblib.load(scp)
logger.info(f"Loaded {name} data: {str(scpf)}")
except Exception as e:
logger.error("Cannot load signal candles from pickled results: ", e)
logger.error(f"Cannot load {name} data from pickled results: ", e)
return signal_candles
return rejected_trades
def _load_rejected_trades(backtest_dir: Path):
return _load_backtest_analysis_data(backtest_dir, "rejected")
def _load_signal_candles(backtest_dir: Path):
return _load_backtest_analysis_data(backtest_dir, "signals")
def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_candles):
@@ -43,9 +51,9 @@ def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_cand
for pair in pairlist:
if pair in signal_candles[strategy_name]:
analysed_trades_dict[strategy_name][pair] = _analyze_candles_and_indicators(
pair,
trades,
signal_candles[strategy_name][pair])
pair,
trades,
signal_candles[strategy_name][pair])
except Exception as e:
print(f"Cannot process entry/exit reasons for {strategy_name}: ", e)
@@ -85,7 +93,7 @@ def _analyze_candles_and_indicators(pair, trades, signal_candles):
return pd.DataFrame()
def _do_group_table_output(bigdf, glist):
def _do_group_table_output(bigdf, glist, to_csv=False, csv_path=None):
for g in glist:
# 0: summary wins/losses grouped by enter tag
if g == "0":
@@ -116,7 +124,8 @@ def _do_group_table_output(bigdf, glist):
sortcols = ['total_num_buys']
_print_table(new, sortcols, show_index=True)
_print_table(new, sortcols, show_index=True, name="Group 0:",
to_csv=to_csv, csv_path=csv_path)
else:
agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
@@ -148,11 +157,23 @@ def _do_group_table_output(bigdf, glist):
new['mean_profit_pct'] = new['mean_profit_pct'] * 100
new['total_profit_pct'] = new['total_profit_pct'] * 100
_print_table(new, sortcols)
_print_table(new, sortcols, name=f"Group {g}:",
to_csv=to_csv, csv_path=csv_path)
else:
logger.warning("Invalid group mask specified.")
def _do_rejected_trades_output(rejected_trades_df, to_csv=False, csv_path=None):
cols = ['pair', 'date', 'enter_tag']
sortcols = ['date', 'pair', 'enter_tag']
_print_table(rejected_trades_df[cols],
sortcols,
show_index=False,
name="Rejected Trades:",
to_csv=to_csv,
csv_path=csv_path)
def _select_rows_within_dates(df, timerange=None, df_date_col: str = 'date'):
if timerange:
if timerange.starttype == 'date':
@@ -186,38 +207,65 @@ def prepare_results(analysed_trades, stratname,
return res_df
def print_results(res_df, analysis_groups, indicator_list):
def print_results(res_df, analysis_groups, indicator_list,
rejected_trades=None, to_csv=False, csv_path=None):
if res_df.shape[0] > 0:
if analysis_groups:
_do_group_table_output(res_df, analysis_groups)
_do_group_table_output(res_df, analysis_groups, to_csv=to_csv, csv_path=csv_path)
if rejected_trades is not None and not rejected_trades.empty:
_do_rejected_trades_output(rejected_trades, to_csv=to_csv, csv_path=csv_path)
# NB this can be large for big dataframes!
if "all" in indicator_list:
print(res_df)
elif indicator_list is not None:
_print_table(res_df,
show_index=False,
name="Indicators:",
to_csv=to_csv,
csv_path=csv_path)
elif indicator_list is not None and indicator_list:
available_inds = []
for ind in indicator_list:
if ind in res_df:
available_inds.append(ind)
ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
_print_table(res_df[ilist], sortcols=['exit_reason'], show_index=False)
_print_table(res_df[ilist],
sortcols=['exit_reason'],
show_index=False,
name="Indicators:",
to_csv=to_csv,
csv_path=csv_path)
else:
print("\\No trades to show")
def _print_table(df, sortcols=None, show_index=False):
def _print_table(df, sortcols=None, show_index=False, name=None, to_csv=False, csv_path=None):
if (sortcols is not None):
data = df.sort_values(sortcols)
else:
data = df
print(
tabulate(
data,
headers='keys',
tablefmt='psql',
showindex=show_index
if to_csv:
if csv_path is not None:
safe_name = Path(csv_path,
name.lower().replace(" ", "_").replace(":", ""))
else:
safe_name = Path("user_data",
"backtest_results",
name.lower().replace(" ", "_").replace(":", ""))
data.to_csv(f"{str(safe_name)}.csv")
else:
if name is not None:
print(name)
print(
tabulate(
data,
headers='keys',
tablefmt='psql',
showindex=show_index
)
)
)
def process_entry_exit_reasons(config: Config):
@@ -226,6 +274,9 @@ def process_entry_exit_reasons(config: Config):
enter_reason_list = config.get('enter_reason_list', ["all"])
exit_reason_list = config.get('exit_reason_list', ["all"])
indicator_list = config.get('indicator_list', [])
do_rejected = config.get('analysis_rejected', False)
to_csv = config.get('analysis_to_csv', False)
csv_path = config.get('analysis_csv_path', config['exportfilename'])
timerange = TimeRange.parse_timerange(None if config.get(
'timerange') is None else str(config.get('timerange')))
@@ -235,8 +286,16 @@ def process_entry_exit_reasons(config: Config):
for strategy_name, results in backtest_stats['strategy'].items():
trades = load_backtest_data(config['exportfilename'], strategy_name)
if not trades.empty:
if trades is not None and not trades.empty:
signal_candles = _load_signal_candles(config['exportfilename'])
rej_df = None
if do_rejected:
rejected_trades_dict = _load_rejected_trades(config['exportfilename'])
rej_df = prepare_results(rejected_trades_dict, strategy_name,
enter_reason_list, exit_reason_list,
timerange=timerange)
analysed_trades_dict = _process_candles_and_indicators(
config['exchange']['pair_whitelist'], strategy_name,
trades, signal_candles)
@@ -247,7 +306,10 @@ def process_entry_exit_reasons(config: Config):
print_results(res_df,
analysis_groups,
indicator_list)
indicator_list,
rejected_trades=rej_df,
to_csv=to_csv,
csv_path=csv_path)
except ValueError as e:
raise OperationalException(e) from e